86 lines
2.9 KiB
Python
86 lines
2.9 KiB
Python
from sklearn.manifold import TSNE
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from sklearn.decomposition import PCA
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import seaborn as sns
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import matplotlib.pyplot as plt
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from run_models import *
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sns.set()
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def load_and_predict(path_like_element):
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# Define Loop to search for models and folder with visualizations
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splitpath = path_like_element.split(os.sep)
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base_dir = os.path.join(*splitpath[:4])
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model = globals()[splitpath[2]]
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print(f'... loading model named: "{Model.name}" from timestamp: {splitpath[3]}')
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pretrained_model = model.load_from_metrics(
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weights_path=path_like_element,
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tags_csv=os.path.join(base_dir, 'default', 'version_0', 'meta_tags.csv'),
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on_gpu=True if torch.cuda.is_available() else False,
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map_location=None
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)
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# Init model and freeze its weights ( for faster inference)
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pretrained_model = pretrained_model.to(device)
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pretrained_model.eval()
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pretrained_model.freeze()
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with torch.no_grad():
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# Load the data for prediction
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# TODO!!!!!!!!!:
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# Hier müssen natürlich auch die date parameter geladen werden!
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# Muss ich die val-sets automatisch neu setzen, also immer auf refresh haben, wenn ich validieren möchte?
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# Was ist denn eigentlich mein Val Dataset?
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# Hab ich irgendwo eine ganze karte?
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# Wie sorge ich dafür, dass gewisse karten, also größenverhältnisse usw nicht überrepräsentiert sind?
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dataset = DataContainer(os.path.join(os.pardir, 'data', 'validation'), 9, 6).to(device)
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dataloader = DataLoader(dataset, shuffle=True, batch_size=len(dataset))
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# Do the inference
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test_pred = [pretrained_model(test_sample)[:-1] for test_sample in dataloader][0]
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for idx, prediction in enumerate(test_pred):
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plot, _ = viz_latent(prediction)
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plot.savefig(os.path.join(base_dir, f'latent_space_{idx}.png'))
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def viz_latent(prediction):
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try:
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prediction = prediction.cpu()
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prediction = prediction.numpy()
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except AttributeError:
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pass
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if prediction.shape[-1] <= 1:
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raise ValueError('How did this happen?')
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elif prediction.shape[-1] == 2:
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ax = sns.scatterplot(x=prediction[:, 0], y=prediction[:, 1])
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try:
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plt.show()
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except:
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pass
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return ax.figure, (ax)
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else:
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fig, axs = plt.subplots(ncols=2)
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plots = []
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for idx, dim_reducer in enumerate([PCA, TSNE]):
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predictions_reduced = dim_reducer(n_components=2).fit_transform(prediction)
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plot = sns.scatterplot(x=predictions_reduced[:, 0], y=predictions_reduced[:, 1],
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ax=axs[idx])
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plot.set_title(dim_reducer.__name__)
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plots.append(plot)
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try:
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plt.show()
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except:
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pass
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return fig, (*plots, )
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if __name__ == '__main__':
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path = 'output'
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search_for_weights(search_for_weights, path) |